
# Quick hack to override default HTML template
# NOT required in some JLab versions.
# https://stackoverflow.com/a/63777508
# https://stackoverflow.com/questions/21971449/how-do-i-increase-the-cell-width-of-the-jupyter-ipython-notebook-in-my-browser
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))

/tmp/ipykernel_9407/28059362.py:5: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display
  from IPython.core.display import display, HTML


# General stats & post-processing to data tables
data.analyseFits()

*** Warning: found MultiIndex for DataFrame data.index - checkDims may have issues with Pandas MultiIndex, but will try anyway.
*** Warning: found MultiIndex for DataFrame data.index - checkDims may have issues with Pandas MultiIndex, but will try anyway.


# The BLMsetPlot routine will output aggregate fit results.
# Here the spread can be taken as a general indication of the uncertainty of 
# the fitting, and indicate whether the fit is well-characterised/the information 
# content of the data is sufficient.
data.BLMsetPlot(xDim = 't', thres=1e-6)  # With xDim and thres set, for more control over outputs


# Write aggregate datasets to HDF5 format
# This is more robust than Pickled data, but PEMtk currently only support output for aggregate (post-processed) fit data.

# 11/04/24 - update dataPath here for current case (manual/slurm datasets)
dataPath = Path(Path.cwd(),dataName + '_1000_080424')

if 'dataFileIn' not in locals().keys():
    if not dataFiles:
        dataFiles = list(dataPath.expanduser().glob('*.pickle'))
        print(dataFiles)
    dataFileIn = dataFiles[-1]

data.processedToHDF5(dataPath = dataPath, outStem = dataFileIn.name, timeStamp=False)


dataFileIn

PosixPath('/home/jovyan/N2O/epsman2024/proc-fock/N2O-fitTest_1000_080424/N2O_559_fit_orb9_8.1_noise_080424_09-45-35.pickle')


# Check stats...
print(f"Basis set size = {len(data.params)} params. Fitting with {magCount} magnitudes and {phaseCount} phases floated.")
print(f"Data size: {data.data[data.subKey]['ADM'].t.size} t-points")
print(f"Data size: {data.data[data.subKey]['ADM'].size} total ADMs (max (K,Q,S): {data.data[data.subKey]['ADM'].ADM.max().values}).")
print(f"Data size: {data.data[data.subKey]['ADM'].size} total BLMs (max (L,M): {data.data[data.subKey]['AFBLM'].BLM.max().values}).")

if addNoise == 'y':
    print(f"Noise added,  mu={mu}, sigma={sigma}")

Basis set size = 38 params. Fitting with 13 magnitudes and 12 phases floated.
Data size: 41 t-points
Data size: 205 total ADMs (max (K,Q,S): (8, 0, 0)).
Data size: 205 total BLMs (max (L,M): (10, 0)).
Noise added,  mu=0, sigma=0.05


# Histogram fit results (reduced chi^2 vs. fit index)
# This may be quite slow for large datasets, setting limited ranges may help

# Use default auto binning
# data.fitHist()

# Example with range set
# data.fitHist(thres=1.15e-6, bins=100)
data.fitHist(binRange=[1.1004e-6, 1.10045e-6], bins=100)  # TODO: options for dp output in plot?

Mask selected 956 results (from 974).


# Check keys
fitNumber = 696
data.data[fitNumber].keys()

dict_keys(['AFBLM', 'residual', 'results'])


# Show some results
data.data[fitNumber]['results']


# Run with defaults
# data.classifyFits()

# For more control, pass bins
# Here the minima is set at one end, and a %age range used for bins
minVal = data.fitsSummary['Stats']['redchi']['min']    

# binRangePC = 0.9e-6   # Gives ~3 per bin for first few best fits
binRangePC = 1.5e-6   # Gives ~6 per bin

data.classifyFits(bins = [minVal, minVal + binRangePC*minVal , 20])

*** Warning: found MultiIndex for DataFrame data.index - checkDims may have issues with Pandas MultiIndex, but will try anyway.
*** Warning: found MultiIndex for DataFrame data.index - checkDims may have issues with Pandas MultiIndex, but will try anyway.


# From the candidates, select a group for analysis
selGroup = 'A'


# paramPlot can be used to check the spread on each parameter.
# Plots use Seaborn or Holoviews/Bokeh
# Colour-mapping is controlled by the 'hue' paramter, additionally pass hRound for sig. fig control.
# The remap setting allows for short-hand labels as set in data.lmmu

paramType = 'm' # Set for (m)agnitude or (p)hase parameters
hRound = 14 # Set for cmapping, default may be too small (leads to all grey cmap on points)

paramPlotBackend = 'hv'  # 'hv' or 'sns'

data.paramPlot(selectors={'Type':paramType, 'redchiGroup':selGroup}, hue = 'redchi', 
               backend=paramPlotBackend, hvType='violin', 
               returnFlag = True, hRound=hRound, remap = 'lmMap');

*** Warning: found MultiIndex for DataFrame data.index - checkDims may have issues with Pandas MultiIndex, but will try anyway.


paramType = 'p' # Set for (m)agnitude or (p)hase parameters
data.paramPlot(selectors={'Type':paramType, 'redchiGroup':selGroup}, hue = 'redchi', backend=paramPlotBackend, hvType='violin', 
               returnFlag = True, hRound=hRound, remap = 'lmMap');

*** Warning: found MultiIndex for DataFrame data.index - checkDims may have issues with Pandas MultiIndex, but will try anyway.


# Run phase correction routine
# Set absFlag=True for unsigned phases (mapped to 0:pi)
# Set useRef=False to set ref phase as 0, otherwise the reference value is set.
phaseCorrParams={'absFlag':True, 'useRef':False}
data.phaseCorrection(**phaseCorrParams)

*** Warning: found MultiIndex for DataFrame data.index - checkDims may have issues with Pandas MultiIndex, but will try anyway.
*** Warning: found MultiIndex for DataFrame data.index - checkDims may have issues with Pandas MultiIndex, but will try anyway.
Set ref param = P_S_P_1_1_n1_1


paramType = 'n'
data.paramPlot(selectors={'Type':paramType, 'redchiGroup':selGroup}, hue = 'redchi', 
               backend=paramPlotBackend, hvType='violin', kind='box',
               returnFlag = True, hRound=hRound, remap = 'lmMap');

WARNING:param.Scatter10901: Setting non-parameter attribute kind=box using a mechanism intended only for parameters

*** Warning: found MultiIndex for DataFrame data.index - checkDims may have issues with Pandas MultiIndex, but will try anyway.


paramType = 'pc'
data.paramPlot(selectors={'Type':paramType, 'redchiGroup':selGroup}, hue = 'redchi', 
               backend=paramPlotBackend, hvType='violin', kind='box',
               returnFlag = True, hRound=hRound, remap = 'lmMap');

WARNING:param.Scatter11608: Setting non-parameter attribute kind=box using a mechanism intended only for parameters

*** Warning: found MultiIndex for DataFrame data.index - checkDims may have issues with Pandas MultiIndex, but will try anyway.


# Parameter summary
data.paramsReport(inds = {'redchiGroup':selGroup})

*** Warning: found MultiIndex for DataFrame data.index - checkDims may have issues with Pandas MultiIndex, but will try anyway.


# Parameter comparison
# Note this uses phaseCorrParams as set previously for consistency
data.paramsCompare(phaseCorrParams=phaseCorrParams)

*** Warning: found MultiIndex for DataFrame data.index - checkDims may have issues with Pandas MultiIndex, but will try anyway.
*** Warning: found MultiIndex for DataFrame data.index - checkDims may have issues with Pandas MultiIndex, but will try anyway.
Set ref param = P_S_P_1_1_n1_1


# Display above results With column name remapping to (l,m) labels only

# With Pandas functionality
data.paramsSummaryComp.rename(columns=data.lmmu['lmMap'])

# With utility method
# summaryRenamed = pemtk.fit._util.renameParams(data.paramsSummaryComp, data.lmmu['lmMap']) 
# summaryRenamed


# Plot values vs. reference cases
# NOTE - experimental code, not yet consolidated and wrapped in PEMtk

paramType = 'm'  # Currently only working for m,p cases?

# Set new DataFrame including "vary" info (missing in default case)
pDict = 'dfWideTest'
# Try using existing function with extra index set...
data._setWide(indexDims = ['Fit','Type','chisqrGroup','redchiGroup','batch', 'vary'], dataWide='dfWideTest')

# plotData = data.paramPlot(dataDict = pDict, selectors={'vary':True, 'Type':'m', 'redchiGroup':selGroup}, hue = 'chisqr', backend='hv', hvType='violin', returnFlag = True, plotScatter=True, remap = 'lmMap', hRound = 12) 
# NO REMAP CASE
plotData = data.paramPlot(dataDict = pDict, selectors={'vary':True, 'Type':paramType, 'redchiGroup':selGroup}, hue = 'chisqr', 
                          backend='hv', hvType='violin', returnFlag = True, plotScatter=True, hRound=hRound)  #, remap='lmMap')   # TODO: add renderPlot or similar option here?
p1 = data.data['plots']['paramPlot']
# p2 = dataTestSub.hvplot.scatter(x='Param',y='value', marker='o', size=200, color='green')

# Plot ref params... CURRENTLY NOT IN paraPlot(), and that also expects fit data so can't reuse directly here.

dataTest = data.data['fits']['dfRef'].copy()
# data.paramPlot(dataDict = 'dfRef')

# Set axis remap
# dataTest.replace({'Param':data.lmmu['lmMap']}, inplace=True)

# Subset
dataTestSub = data._subsetFromXS(selectors = {'Type':paramType}, data = dataTest)  
p2 = dataTestSub.hvplot.scatter(x='Param',y='value', marker='dash', size=500, color='red')

# p1+p2   # Overlays fail with "NotImplementedError: Iteration on Elements is not supported." Issue with plot types? FIXED - issues was non-plot return from paramPlot()!
p1*p2

*** Warning: found MultiIndex for DataFrame data.index - checkDims may have issues with Pandas MultiIndex, but will try anyway.
*** Warning: found MultiIndex for DataFrame data.index - checkDims may have issues with Pandas MultiIndex, but will try anyway.
*** Warning: found MultiIndex for DataFrame data.index - checkDims may have issues with Pandas MultiIndex, but will try anyway.

*** Warning: found MultiIndex for DataFrame data.index - checkDims may have issues with Pandas MultiIndex, but will try anyway.


# self.paramsSummary contains the results above as Pandas Dataframe, usual Pandas methods can be applied.
data.paramsSummary['data'].describe()


# To set matrix elements from aggregate fit results, use `seetAggMatE` for Pandas
data.setAggMatE(simpleForm = True)
data.data['agg']['matEpd']


# To set matrix elements from aggregate fit results, use `aggToXR` for Xarray
# data.aggToXR(refKey = 'orb5', returnType = 'ds', conformDims=True)   # use full ref dataset
data.aggToXR(refKey = 'subset', returnType = 'ds', conformDims=True)   # Subselected matE

Added dim Total
Added dim Targ
Added dim Total
Added dim Targ
Set XR dataset for self.data['agg']['matE']


data.data['agg']['matE']

<xarray.Dataset>
Dimensions:  (Type: 2, LM: 21, Sym: 4, mu: 3, it: 1)
Coordinates:
  * Type     (Type) object 'comp' 'compC'
  * LM       (LM) MultiIndex
  - l        (LM) int64 0 0 0 1 1 1 2 2 2 3 3 3 4 4 4 5 5 5 6 6 6
  - m        (LM) int64 -1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1
  * Sym      (Sym) MultiIndex
  - Cont     (Sym) object 'P' 'P' 'S' 'S'
  - Targ     (Sym) object 'S' 'U' 'S' 'U'
  - Total    (Sym) object 'P' 'U' 'S' 'U'
  * mu       (mu) int64 -1 0 1
  * it       (it) int64 1
    Eke      float64 8.1
    Ehv      float64 27.1
    SF       complex128 (2.8348434+2.8457598j)
Data variables:
    comp     (Type, mu, LM, Sym) complex128 (nan+nanj) (nan+nanj) ... (nan+nanj)
    compC    (Type, mu, LM, Sym) complex128 (nan+nanj) (nan+nanj) ... (nan+nanj)
    subset   (LM, Sym, mu, it) complex128 (nan+nanj) (nan+nanj) ... (nan+nanj)

array(['comp', 'compC'], dtype=object)

array([(0, -1), (0, 0), (0, 1), (1, -1), (1, 0), (1, 1), (2, -1), (2, 0),
       (2, 1), (3, -1), (3, 0), (3, 1), (4, -1), (4, 0), (4, 1), (5, -1),
       (5, 0), (5, 1), (6, -1), (6, 0), (6, 1)], dtype=object)

array([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6])

array([-1,  0,  1, -1,  0,  1, -1,  0,  1, -1,  0,  1, -1,  0,  1, -1,  0,  1,
       -1,  0,  1])

array([('P', 'S', 'P'), ('P', 'U', 'U'), ('S', 'S', 'S'), ('S', 'U', 'U')],
      dtype=object)

array(['P', 'P', 'S', 'S'], dtype=object)

array(['S', 'U', 'S', 'U'], dtype=object)

array(['P', 'U', 'S', 'U'], dtype=object)

array([-1,  0,  1])


# import numpy as np

def unsignedPhase(da):
    """Convert to unsigned phases."""
    # Set mag, phase
    mag = da.pipe(np.abs)
    phase = da.pipe(np.angle)  # Returns np array only!
    
    # Set unsigned
    magUS = mag.pipe(np.abs)
#     phaseUS = phase.pipe(np.abs)  
    phaseUS = np.abs(phase)
    
    # Set complex
    compFixed = magUS * np.exp(1j* phaseUS)
#     return mag,phase
    return compFixed


# v2 - as v1, but differences for unsigned phase case & fix labels
# 26/07/22: messy but working. Some labelling tricks to push back into matPlot() routine

# Import routines
from epsproc.calc import density


# Compose density matrix

# Set dimensions/state vector/representation
# These must be in original data, but will be restacked as necessary to define the effective basis space.
denDims = ['LM', 'mu']
selDims = {'Type':'L'}
pTypes=['r','i']
thres = 1e-2    # 0.2 # Threshold out l>3 terms if using full 'orb5' set.
normME = False
normDen = 'max'
usPhase = True # Use unsigned phases?

# Calculate - Ref case
# matE = data.data['subset']['matE']
# Set data from master class
# k = 'orb5'  # N2 orb5 (SG) dataset
k = 'subset'
matE = data.data[k]['matE']
if normME:
    matE = matE/matE.max()

if usPhase:
    matE = unsignedPhase(matE)
    
daOut, *_ = density.densityCalc(matE, denDims = denDims, selDims = selDims, thres = thres)  # OK

if normDen=='max':
    daOut = daOut/daOut.max()
elif normDen=='trace':
    daOut = daOut/(daOut.sum('Sym').pipe(np.trace)**2)  # Need sym sum here to get 2D trace
    
# daPlot = density.matPlot(daOut.sum('Sym'))
daPlot = density.matPlot(daOut.sum('Sym'), pTypes=pTypes)

# Retrieved
matE = data.data['agg']['matE']['compC']
selDims = {'Type':'compC'}  # For stacked DS case need to set selDims again here to avoid null data selection below.
if normME:
    matE = matE/matE.max()
    
if usPhase:
    matE = unsignedPhase(matE)
    
daOut2, *_ = density.densityCalc(matE, denDims = denDims, selDims = selDims, thres = thres)  # OK

if normDen=='max':
    daOut2 = daOut2/daOut2.max()
elif normDen=='trace':
    daOut2 = daOut2/(daOut2.sum('Sym').pipe(np.trace)**2)
    
daPlot2 = density.matPlot(daOut2.sum('Sym'), pTypes=pTypes)   #.sel(Eke=slice(0.5,1.5,1)))


# Compute difference
if usPhase:
    daDiff = unsignedPhase(daOut.sum('Sym')) - unsignedPhase(daOut2.sum('Sym'))

else:
    daDiff = daOut.sum('Sym') - daOut2.sum('Sym')

daDiff.name = 'Difference'
daPlotDiff = density.matPlot(daDiff, pTypes=pTypes)



#******** Plot
daLayout = (daPlot.redim(pType='Component').layout('Component').relabel('(a) Reference density matrix (unsigned phases)') + daPlot2.opts(show_title=False).layout('pType').opts(show_title=True).relabel('(b) Reconstructed') + 
                daPlotDiff.opts(show_title=False).layout('pType').opts(show_title=True).relabel('(c) Difference')).cols(1)  
daLayout.opts(ep.plot.hvPlotters.opts.HeatMap(width=300, frame_width=300, aspect='square', tools=['hover'], colorbar=True, cmap='coolwarm'))  # .opts(show_title=False)  # .opts(title="Custom Title")  #OK



# Notes on titles... see https://holoviews.org/user_guide/Customizing_Plots.html
#
# .relabel('Test') and .opts(title="Custom Title") OK for whole row titles
#
# daPlot2.opts(show_title=False).layout('pType').opts(show_title=True).relabel('Recon')  Turns off titles per plot, then titles layout
#
# .redim() to modify individual plot group label (from dimension name)

Set plot kdims to ['l,m,mu', 'l,m,mu_p']; pass kdims = [dim1,dim2] for more control.
Set plot kdims to ['l,m,mu', 'l,m,mu_p']; pass kdims = [dim1,dim2] for more control.
Set plot kdims to ['l,m,mu', 'l,m,mu_p']; pass kdims = [dim1,dim2] for more control.

WARNING:param.HeatMapPlot22447: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.
WARNING:param.HeatMapPlot22455: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.
WARNING:param.HeatMapPlot22484: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.
WARNING:param.HeatMapPlot22491: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.
WARNING:param.HeatMapPlot22519: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.
WARNING:param.HeatMapPlot22526: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.


dataIn = data.data['agg']['matE'].copy()

# # Restack for MFPAD plotter
# from epsproc.util.listFuncs import dataTypesList

# refDims = dataTypesList()
# refDims = refDims['matE']['def']
# dataStacked = dataIn.stack(refDims(sType='sDict'))
# dataStacked

# # Style 1: if full ref dataset included (with Eke dim)
# # Create empty ePSbase class instance, and set data
# # Can then use existing  padPlot() routine for all data
# from epsproc.classes.base import ePSbase
# dataTest = ePSbase(verbose = 1)

# aList = [i for i in dataIn.data_vars]  # List of arrays

# # Loop version & propagate attrs
# dataType = 'matE'
# for item in aList:
#     if item.startswith('orb'):
#         selType='L'
#     else:
#         selType = item
        
#     dataTest.data[item] = {dataType : dataIn[item].sel({'Type':selType})}

# Style 2: single Eke dim case
# Create empty ePSbase class instance, and set data
# Can then use existing  padPlot() routine for all data
from epsproc.classes.base import ePSbase
dataTest = ePSbase(verbose = 1)

aList = [i for i in dataIn.data_vars]  # List of arrays

# Loop version & propagate attrs
dataType = 'matE'
for item in aList:
    if item.startswith('sub'):
        dataTest.data[item] = {dataType : dataIn[item]}
    else:
        selType = item
        dataTest.data[item] = {dataType : dataIn[item].sel({'Type':selType})}
        
    # Push singleton Eke value to dim for plotter
    dataTest.data[item][dataType] = dataTest.data[item][dataType].expand_dims('Eke')


aList

['comp', 'compC', 'subset']


pKey

'comp'


# Set polarization geoms from Euler angles

# import numpy as np
# import epsproc as ep

# Set Euler angs to include diagonal pol case
pRot = [0, 0, np.pi/2, 0]
tRot = [0, np.pi/2, np.pi/2, np.pi/4]
cRot = [0, 0, 0, 0]
labels = ['z','x','y', 'd']
eulerAngs = np.array([labels, pRot, tRot, cRot]).T   # List form to use later, rows per set of angles


# Should also use MFBLM function below instead of numeric version?
# Numeric version is handy for direct surface and difference case.
R = ep.setPolGeoms(eulerAngs = eulerAngs)
# R

# Basic version - working, but get separate plots per set.
# UPDATE: use this to generate all raw figures, then restack plotly objects below

# Erange=[1,2,1]  # Set for a single E-point
Erange=[Eke-0.5,Eke+0.5,1]

# Comparison and diff
pKey = [i for i in dataIn.data_vars if i!='comp']  # List of arrays
dataTest.mfpadNumeric(keys=pKey, R = R)   # Compute MFPADs for each set of matrix elements using numerical routine
# ep.mfpad

dataTest.data['diff'] = {'TX': dataTest.data['subset']['TX'].sum('Sym')-dataTest.data['compC']['TX'].sum('Sym')}  # Add sum over sym to force matching dims
pKey.extend(['diff'])

# Plot
print(f"\n*** Plotting for keys = {pKey}, one per row ***\n")  # Note plot labels could do with some work!
dataTest.padPlot(keys=pKey, Erange=Erange, backend='pl',returnFlag=True, plotFlag=True, facetDims=['Labels',None])  # WORKING: facetDims=['Eke',None])  # DEFAULT FAILING: facetDims=['Labels', 'Eke']) # Generate plotly polar surf plots for each dataset

*** Plotting for keys = ['compC', 'subset', 'diff'], one per row ***

Summing over dims: {'Sym'}
Found additional dims {'Eke'}, summing to reduce for plot. Pass selDims to avoid.
Plotting from self.data[compC][TX], facetDims=['Labels', None], pType=a2 with backend=pl.
*** WARNING: plot dataset has min value < 0, min = (-0.4393266707109916+0.9417986670190246j). This may be unphysical and/or result in plotting issues.

Set plot to self.data['compC']['plots']['TX']['polar']
Summing over dims: {'Sym'}
Found additional dims {'Eke'}, summing to reduce for plot. Pass selDims to avoid.
Plotting from self.data[subset][TX], facetDims=['Labels', None], pType=a2 with backend=pl.
*** WARNING: plot dataset has min value < 0, min = (-0.6394780210267588+0.5477229755385662j). This may be unphysical and/or result in plotting issues.

Set plot to self.data['subset']['plots']['TX']['polar']
Summing over dims: set()
Found additional dims {'Eke'}, summing to reduce for plot. Pass selDims to avoid.
Plotting from self.data[diff][TX], facetDims=['Labels', None], pType=a2 with backend=pl.
*** WARNING: plot dataset has min value < 0, min = (-0.7282983645421228+0.4263031250551567j). This may be unphysical and/or result in plotting issues.

Set plot to self.data['diff']['plots']['TX']['polar']


# print(data.jobInfo['ePolyScat'][0])
# print('Run: ' + jobInfo['Starting'][0].split('at')[1])


import scooby
scooby.Report(additional=['epsproc', 'xarray', 'jupyter', 'holoviews', 'pandas'])


# Check current Git commit for local ePSproc version
!git -C {Path(ep.__file__).parent} branch
!git -C {Path(ep.__file__).parent} log --format="%H" -n 1

* 3d-AFPAD-dev
6fab386afe5791abfe5bf3c1c289c8c92054277f


# Check current remote commits
!git ls-remote --heads https://github.com/phockett/ePSproc

6fab386afe5791abfe5bf3c1c289c8c92054277f	refs/heads/3d-AFPAD-dev
226759a58ebe96bf1a6df60ccd5efb0c449af3a7	refs/heads/dependabot/pip/notes/envs/envs-versioned/pillow-10.3.0
7e4270370d66df44c334675ac487c87d702408da	refs/heads/dev
1c0b8fd409648f07c85f4f20628b5ea7627e0c4e	refs/heads/master
69cd89ce5bc0ad6d465a4bd8df6fba15d3fd1aee	refs/heads/numba-tests
ea30878c842f09d525fbf39fa269fa2302a13b57	refs/heads/revert-9-master
baf0be0c962e8ab3c3df57c8f70f0e939f99cbd7	refs/heads/testDev

fitting method	leastsq
# function evals	2852
# data points	451
# variables	25
chi-square	4.6876e-04
reduced chi-square	1.1004e-06
Akaike info crit.	-6163.37197
Bayesian info crit.	-6060.58529

name	value	standard error	relative error	initial value	min	max	vary	expression
m_P_S_P_1_n1_1_1	1.47672449	3.0485e+08	(20643663529.67%)	0.8860762861939793	1.0000e-04	5.00000000	True
m_P_S_P_1_1_n1_1	1.47672449	3.0485e+08	(20643663487.72%)	0.8860762861939793	1.0000e-04	5.00000000	False	m_P_S_P_1_n1_1_1
m_P_S_P_2_n1_1_1	0.36671056	5.9793e+08	(163052950914.54%)	0.16069660327581758	1.0000e-04	5.00000000	True
m_P_S_P_2_1_n1_1	0.36671056	5.9793e+08	(163052950627.77%)	0.16069660327581758	1.0000e-04	5.00000000	False	m_P_S_P_2_n1_1_1
m_P_S_P_3_n1_1_1	0.03511796	96545978.0	(274919085305.16%)	0.620226297126592	1.0000e-04	5.00000000	True
m_P_S_P_3_1_n1_1	0.03511796	96545977.8	(274919084835.74%)	0.620226297126592	1.0000e-04	5.00000000	False	m_P_S_P_3_n1_1_1
m_P_S_P_4_n1_1_1	0.58250076	3.2190e+08	(55262131863.93%)	0.39760026959116823	1.0000e-04	5.00000000	True
m_P_S_P_4_1_n1_1	0.58250076	3.2190e+08	(55262131847.24%)	0.39760026959116823	1.0000e-04	5.00000000	False	m_P_S_P_4_n1_1_1
m_P_S_P_5_n1_1_1	0.34759066	1.0770e+08	(30984570838.56%)	0.6241133656185965	1.0000e-04	5.00000000	True
m_P_S_P_5_1_n1_1	0.34759066	1.0770e+08	(30984570785.71%)	0.6241133656185965	1.0000e-04	5.00000000	False	m_P_S_P_5_n1_1_1
m_P_S_P_6_n1_1_1	0.37017292	6.3920e+08	(172676967335.07%)	0.5467541650735425	1.0000e-04	5.00000000	True
m_P_S_P_6_1_n1_1	0.37017292	6.3920e+08	(172676967222.45%)	0.5467541650735425	1.0000e-04	5.00000000	False	m_P_S_P_6_n1_1_1
m_S_S_S_0_0_0_1	1.52449768	4.7281e+08	(31014209552.33%)	0.6656859904053511	1.0000e-04	5.00000000	True
m_S_S_S_1_0_0_1	1.77896077	1.7710e+08	(9955051029.54%)	0.5516835821546856	1.0000e-04	5.00000000	True
m_S_S_S_2_0_0_1	1.73121799	4.4318e+08	(25599061902.89%)	0.5518461069601724	1.0000e-04	5.00000000	True
m_S_S_S_3_0_0_1	1.14244402	2.8316e+08	(24785421198.42%)	0.5939359850592106	1.0000e-04	5.00000000	True
m_S_S_S_4_0_0_1	1.13376497	2.4828e+08	(21898681956.61%)	0.17188025192261902	1.0000e-04	5.00000000	True
m_S_S_S_5_0_0_1	0.43811343	7.1134e+08	(162364836768.44%)	0.4455936261077418	1.0000e-04	5.00000000	True
m_S_S_S_6_0_0_1	0.16064202	3.7275e+08	(232037139803.13%)	0.9612133269422948	1.0000e-04	5.00000000	True
p_P_S_P_1_n1_1_1	-2.85739317	0.00000000	(0.00%)	-2.857393167231562	-3.14159265	3.14159265	False
p_P_S_P_1_1_n1_1	-2.85739317	0.00000000	(0.00%)	-2.857393167231562	-3.14159265	3.14159265	False	p_P_S_P_1_n1_1_1
p_P_S_P_2_n1_1_1	1.70523025	2.6604e+08	(15601541116.80%)	0.7700854199580771	-3.14159265	3.14159265	True
p_P_S_P_2_1_n1_1	1.70523025	2.6604e+08	(15601541100.35%)	0.7700854199580771	-3.14159265	3.14159265	False	p_P_S_P_2_n1_1_1
p_P_S_P_3_n1_1_1	-0.23826276	2.6092e+08	(109508752144.22%)	0.1704705215973239	-3.14159265	3.14159265	True
p_P_S_P_3_1_n1_1	-0.23826276	2.6092e+08	(109508752340.72%)	0.1704705215973239	-3.14159265	3.14159265	False	p_P_S_P_3_n1_1_1
p_P_S_P_4_n1_1_1	-0.32836413	3.0618e+08	(93244875233.47%)	0.5035801054886633	-3.14159265	3.14159265	True
p_P_S_P_4_1_n1_1	-0.32836413	3.0618e+08	(93244875017.84%)	0.5035801054886633	-3.14159265	3.14159265	False	p_P_S_P_4_n1_1_1
p_P_S_P_5_n1_1_1	0.29479429	3.3014e+08	(111990060820.57%)	0.23470718604773666	-3.14159265	3.14159265	True
p_P_S_P_5_1_n1_1	0.29479429	3.3014e+08	(111990061073.08%)	0.23470718604773666	-3.14159265	3.14159265	False	p_P_S_P_5_n1_1_1
p_P_S_P_6_n1_1_1	3.12014299	4.9044e+08	(15718633629.78%)	0.7051031845542469	-3.14159265	3.14159265	True
p_P_S_P_6_1_n1_1	3.12014299	4.9044e+08	(15718633605.37%)	0.7051031845542469	-3.14159265	3.14159265	False	p_P_S_P_6_n1_1_1
p_S_S_S_0_0_0_1	1.28842864	5.9702e+08	(46337338605.79%)	0.43063262562515603	-3.14159265	3.14159265	True
p_S_S_S_1_0_0_1	0.28338855	1.7468e+08	(61638350418.97%)	0.7355325030452876	-3.14159265	3.14159265	True
p_S_S_S_2_0_0_1	-1.46879940	4.6032e+08	(31339918828.80%)	0.06889941894672202	-3.14159265	3.14159265	True
p_S_S_S_3_0_0_1	-0.17951447	1.2102e+08	(67416928787.13%)	0.033275669140320874	-3.14159265	3.14159265	True
p_S_S_S_4_0_0_1	1.02789099	5.0388e+08	(49020502367.99%)	0.3138781475801862	-3.14159265	3.14159265	True
p_S_S_S_5_0_0_1	1.47848227	4.4368e+08	(30008931072.01%)	0.7675345196637045	-3.14159265	3.14159265	True
p_S_S_S_6_0_0_1	-0.30925984	3.4011e+08	(109976955515.33%)	0.6836014802961643	-3.14159265	3.14159265	True

	success				chisqr				redchi
	count	unique	top	freq	count	unique	top	freq	count	unique	top	freq
redchiGroup
A	6	1	True	6	6.0	6.0	0.000469	1.0	6.0	6.0	0.000001	1.0
B	4	1	True	4	4.0	4.0	0.000469	1.0	4.0	4.0	0.000001	1.0
C	6	1	True	6	6.0	6.0	0.000469	1.0	6.0	6.0	0.000001	1.0
D	8	1	True	8	8.0	8.0	0.000469	1.0	8.0	8.0	0.000001	1.0
E	10	1	True	10	10.0	10.0	0.000469	1.0	10.0	10.0	0.000001	1.0
F	10	1	True	10	10.0	10.0	0.000469	1.0	10.0	10.0	0.000001	1.0
G	15	1	True	15	15.0	15.0	0.000469	1.0	15.0	15.0	0.000001	1.0
H	16	1	True	16	16.0	16.0	0.000469	1.0	16.0	16.0	0.000001	1.0
I	15	1	True	15	15.0	15.0	0.000469	1.0	15.0	15.0	0.000001	1.0
J	15	1	True	15	15.0	15.0	0.000469	1.0	15.0	15.0	0.000001	1.0
K	25	1	True	25	25.0	25.0	0.000469	1.0	25.0	25.0	0.000001	1.0
L	17	1	True	17	17.0	17.0	0.000469	1.0	17.0	17.0	0.000001	1.0
M	23	1	True	23	23.0	23.0	0.000469	1.0	23.0	23.0	0.000001	1.0
N	28	1	True	28	28.0	28.0	0.000469	1.0	28.0	28.0	0.000001	1.0
O	17	1	True	17	17.0	17.0	0.000469	1.0	17.0	17.0	0.000001	1.0
P	19	1	True	19	19.0	19.0	0.000469	1.0	19.0	19.0	0.000001	1.0
Q	11	1	True	11	11.0	11.0	0.000469	1.0	11.0	11.0	0.000001	1.0
R	22	1	True	22	22.0	22.0	0.000469	1.0	22.0	22.0	0.000001	1.0
S	14	1	True	14	14.0	14.0	0.000469	1.0	14.0	14.0	0.000001	1.0

		Param	P_S_P_1_1_n1_1	P_S_P_1_n1_1_1	P_S_P_2_1_n1_1	P_S_P_2_n1_1_1	P_S_P_3_1_n1_1	P_S_P_3_n1_1_1	P_S_P_4_1_n1_1	P_S_P_4_n1_1_1	P_S_P_5_1_n1_1	P_S_P_5_n1_1_1	P_S_P_6_1_n1_1	P_S_P_6_n1_1_1	S_S_S_0_0_0_1	S_S_S_1_0_0_1	S_S_S_2_0_0_1	S_S_S_3_0_0_1	S_S_S_4_0_0_1	S_S_S_5_0_0_1	S_S_S_6_0_0_1
Fit	Type	redchiGroup
0	m	O	0.587109	0.587109	0.876792	0.876792	0.826907	0.826907	0.819588	0.819588	0.557993	0.557993	0.364997	0.364997	1.749348	1.110452	1.540857	1.284653	1.464809	0.892935	0.182733
	n	O	0.141889	0.141889	0.211898	0.211898	0.199842	0.199842	0.198074	0.198074	0.134853	0.134853	0.088211	0.088211	0.422773	0.268368	0.372386	0.310468	0.354007	0.215800	0.044162
	p	O	-2.857393	-2.857393	1.473154	1.473154	2.109622	2.109622	-1.844451	-1.844451	-0.706675	-0.706675	1.182573	1.182573	2.511460	0.363996	0.229466	0.475833	2.130921	2.365797	-1.358567
	pc	O	0.000000	0.000000	1.952638	1.952638	1.316170	1.316170	1.012942	1.012942	2.150718	2.150718	2.243219	2.243219	0.914332	3.061796	3.086860	2.949959	1.294871	1.059995	1.498826
2	m	P	1.465974	1.465974	0.374354	0.374354	0.165118	0.165118	0.573602	0.573602	0.356709	0.356709	0.375444	0.375444	2.088999	1.315278	0.440169	1.114193	1.746866	0.825574	0.178978
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
994	pc	J	0.000000	0.000000	1.801854	1.801854	2.748622	2.748622	0.307106	0.307106	3.106621	3.106621	2.966311	2.966311	1.274185	2.988401	1.494340	2.859567	1.031810	1.642137	0.776052
997	m	A	1.071010	1.071010	1.058500	1.058500	0.217025	0.217025	0.665377	0.665377	0.147383	0.147383	0.366794	0.366794	1.085956	1.453644	2.060101	1.511633	1.135005	0.398858	0.159724
	n	A	0.258835	0.258835	0.255812	0.255812	0.052449	0.052449	0.160804	0.160804	0.035619	0.035619	0.088645	0.088645	0.262448	0.351308	0.497873	0.365323	0.274302	0.096394	0.038601
	p	A	-2.857393	-2.857393	-1.464188	-1.464188	0.428979	0.428979	0.890176	0.890176	-0.526859	-0.526859	3.141593	3.141593	-0.945850	-0.661391	2.185180	-1.253769	-1.084264	-0.028552	0.198967
	pc	A	0.000000	0.000000	1.393205	1.393205	2.996813	2.996813	2.535616	2.535616	2.330534	2.330534	0.284199	0.284199	1.911543	2.196002	1.240612	1.603625	1.773129	2.828841	3.056360

	Param	P_S_P_1_1_n1_1	P_S_P_1_n1_1_1	P_S_P_2_1_n1_1	P_S_P_2_n1_1_1	P_S_P_3_1_n1_1	P_S_P_3_n1_1_1	P_S_P_4_1_n1_1	P_S_P_4_n1_1_1	P_S_P_5_1_n1_1	P_S_P_5_n1_1_1	P_S_P_6_1_n1_1	P_S_P_6_n1_1_1	S_S_S_0_0_0_1	S_S_S_1_0_0_1	S_S_S_2_0_0_1	S_S_S_3_0_0_1	S_S_S_4_0_0_1	S_S_S_5_0_0_1	S_S_S_6_0_0_1
Fit	Type
ref	m	0.552053	0.552053	1.179614	1.179614	0.773419	0.773419	0.743628	0.743628	0.199496	0.199496	0.022219	0.022219	1.829333	0.485076	2.489088	0.577691	1.000426	0.236576	0.050528
	n	0.134106	0.134106	0.286555	0.286555	0.187881	0.187881	0.180644	0.180644	0.048462	0.048462	0.005397	0.005397	0.444387	0.117836	0.604657	0.140335	0.243026	0.057470	0.012274
	p	-2.857393	-2.857393	1.843649	1.843649	-0.504852	-0.504852	-1.394619	-1.394619	1.889342	1.889342	2.309593	2.309593	-0.381123	1.609179	-2.177673	1.497285	-0.235498	2.108398	-2.401628
	pc	0.000000	0.000000	1.582143	1.582143	2.352541	2.352541	1.462774	1.462774	1.536450	1.536450	1.116199	1.116199	2.476270	1.816613	0.679721	1.928507	2.621895	1.317394	0.455766

N2O AF fitting tests (data analysis plots only)¶

Notes¶

Post-processing and data overview¶

Data exploration¶

View results¶

Fit Result

Classify candidate sets¶

Explore candidate result sets¶

Raw results¶

Phases, phase shifts & corrections¶

Parameter estimation & fidelity¶

Best values and statistics¶

Using the reconstructed matrix elements¶

Density matrices¶

Plot MF PADs¶

Versions¶

Code¶

Parameter1	Parameter 2	Correlation
m_P_S_P_4_n1_1_1	m_P_S_P_6_n1_1_1	+0.9500
m_S_S_S_5_0_0_1	m_S_S_S_6_0_0_1	-0.9096
m_P_S_P_1_n1_1_1	m_P_S_P_6_n1_1_1	-0.9071
m_S_S_S_3_0_0_1	m_S_S_S_4_0_0_1	+0.8774
m_P_S_P_1_n1_1_1	p_S_S_S_2_0_0_1	+0.8649
m_P_S_P_1_n1_1_1	m_P_S_P_4_n1_1_1	-0.8594
m_P_S_P_6_n1_1_1	p_S_S_S_2_0_0_1	-0.8498
m_P_S_P_2_n1_1_1	m_P_S_P_3_n1_1_1	+0.8411
m_P_S_P_5_n1_1_1	m_S_S_S_3_0_0_1	+0.8211
m_P_S_P_4_n1_1_1	p_S_S_S_2_0_0_1	-0.8113
m_P_S_P_5_n1_1_1	m_P_S_P_6_n1_1_1	+0.7894
m_S_S_S_5_0_0_1	p_S_S_S_4_0_0_1	+0.7710
p_P_S_P_2_n1_1_1	p_S_S_S_2_0_0_1	+0.7649
m_P_S_P_5_n1_1_1	m_S_S_S_4_0_0_1	+0.7610
m_P_S_P_1_n1_1_1	m_P_S_P_3_n1_1_1	-0.7121
m_S_S_S_1_0_0_1	p_P_S_P_6_n1_1_1	+0.7042
m_P_S_P_4_n1_1_1	m_P_S_P_5_n1_1_1	+0.7038
m_P_S_P_4_n1_1_1	m_S_S_S_3_0_0_1	+0.7006
m_S_S_S_6_0_0_1	p_S_S_S_4_0_0_1	-0.7005
m_P_S_P_6_n1_1_1	m_S_S_S_3_0_0_1	+0.6814
m_P_S_P_5_n1_1_1	p_S_S_S_4_0_0_1	-0.6652
m_P_S_P_2_n1_1_1	m_P_S_P_5_n1_1_1	-0.6604
m_S_S_S_1_0_0_1	p_S_S_S_5_0_0_1	-0.6575
m_P_S_P_6_n1_1_1	p_S_S_S_4_0_0_1	-0.6551
m_P_S_P_2_n1_1_1	m_S_S_S_3_0_0_1	-0.6378
m_P_S_P_3_n1_1_1	p_P_S_P_2_n1_1_1	-0.6287
m_S_S_S_4_0_0_1	p_S_S_S_0_0_0_1	+0.5975
m_P_S_P_6_n1_1_1	m_S_S_S_4_0_0_1	+0.5922
p_P_S_P_6_n1_1_1	p_S_S_S_5_0_0_1	-0.5856
m_S_S_S_3_0_0_1	p_S_S_S_2_0_0_1	-0.5844
m_P_S_P_2_n1_1_1	p_S_S_S_0_0_0_1	-0.5721
m_S_S_S_0_0_0_1	m_S_S_S_6_0_0_1	+0.5679
m_S_S_S_0_0_0_1	p_S_S_S_5_0_0_1	+0.5630
m_P_S_P_4_n1_1_1	p_S_S_S_4_0_0_1	-0.5612
m_S_S_S_4_0_0_1	p_S_S_S_2_0_0_1	-0.5611
m_P_S_P_5_n1_1_1	p_S_S_S_2_0_0_1	-0.5585
m_P_S_P_3_n1_1_1	m_S_S_S_1_0_0_1	+0.5500
m_P_S_P_4_n1_1_1	m_S_S_S_4_0_0_1	+0.5495
m_S_S_S_0_0_0_1	m_S_S_S_5_0_0_1	-0.5479
m_P_S_P_5_n1_1_1	p_S_S_S_0_0_0_1	+0.5460
m_P_S_P_3_n1_1_1	p_S_S_S_2_0_0_1	-0.5404
m_S_S_S_0_0_0_1	m_S_S_S_2_0_0_1	-0.5336
m_S_S_S_3_0_0_1	p_S_S_S_0_0_0_1	+0.5201
m_P_S_P_1_n1_1_1	p_P_S_P_2_n1_1_1	+0.5138
m_P_S_P_2_n1_1_1	m_S_S_S_4_0_0_1	-0.5108
m_S_S_S_6_0_0_1	p_S_S_S_0_0_0_1	-0.5083
m_P_S_P_1_n1_1_1	p_S_S_S_4_0_0_1	+0.5077
m_S_S_S_2_0_0_1	m_S_S_S_4_0_0_1	-0.5068
m_P_S_P_2_n1_1_1	m_S_S_S_1_0_0_1	+0.5065
m_S_S_S_0_0_0_1	p_S_S_S_1_0_0_1	-0.5003
m_S_S_S_1_0_0_1	p_S_S_S_6_0_0_1	-0.4942
p_S_S_S_2_0_0_1	p_S_S_S_4_0_0_1	+0.4902
m_S_S_S_3_0_0_1	p_S_S_S_3_0_0_1	-0.4901
p_S_S_S_1_0_0_1	p_S_S_S_3_0_0_1	-0.4846
m_P_S_P_4_n1_1_1	p_S_S_S_0_0_0_1	+0.4838
m_S_S_S_0_0_0_1	p_S_S_S_0_0_0_1	-0.4736
m_S_S_S_1_0_0_1	m_S_S_S_2_0_0_1	-0.4725
m_P_S_P_1_n1_1_1	m_P_S_P_5_n1_1_1	-0.4685
m_S_S_S_6_0_0_1	p_S_S_S_5_0_0_1	+0.4638
m_S_S_S_1_0_0_1	p_S_S_S_4_0_0_1	+0.4584
m_S_S_S_3_0_0_1	p_S_S_S_1_0_0_1	+0.4455
p_P_S_P_3_n1_1_1	p_P_S_P_4_n1_1_1	-0.4408
m_P_S_P_5_n1_1_1	p_P_S_P_6_n1_1_1	-0.4401
p_P_S_P_4_n1_1_1	p_S_S_S_5_0_0_1	+0.4320
m_P_S_P_4_n1_1_1	m_S_S_S_5_0_0_1	-0.4313
p_P_S_P_2_n1_1_1	p_P_S_P_4_n1_1_1	+0.4312
m_P_S_P_3_n1_1_1	p_S_S_S_5_0_0_1	-0.4310
m_P_S_P_2_n1_1_1	p_P_S_P_2_n1_1_1	-0.4229
m_P_S_P_6_n1_1_1	p_S_S_S_0_0_0_1	+0.4093
m_P_S_P_6_n1_1_1	m_S_S_S_5_0_0_1	-0.4091
m_P_S_P_1_n1_1_1	m_S_S_S_3_0_0_1	-0.4063
m_P_S_P_1_n1_1_1	m_S_S_S_1_0_0_1	-0.3961
p_P_S_P_4_n1_1_1	p_P_S_P_6_n1_1_1	-0.3952
p_P_S_P_4_n1_1_1	p_S_S_S_2_0_0_1	+0.3909
m_S_S_S_6_0_0_1	p_S_S_S_6_0_0_1	+0.3906
m_P_S_P_1_n1_1_1	m_S_S_S_6_0_0_1	-0.3851
m_P_S_P_2_n1_1_1	p_P_S_P_6_n1_1_1	+0.3833
m_P_S_P_1_n1_1_1	p_S_S_S_5_0_0_1	+0.3817
p_S_S_S_0_0_0_1	p_S_S_S_5_0_0_1	-0.3779
m_P_S_P_3_n1_1_1	m_P_S_P_6_n1_1_1	+0.3753
m_P_S_P_6_n1_1_1	m_S_S_S_6_0_0_1	+0.3747
m_S_S_S_3_0_0_1	p_S_S_S_4_0_0_1	-0.3738
p_P_S_P_6_n1_1_1	p_S_S_S_6_0_0_1	-0.3716
m_P_S_P_1_n1_1_1	m_S_S_S_5_0_0_1	+0.3648
m_S_S_S_1_0_0_1	m_S_S_S_6_0_0_1	-0.3556
m_P_S_P_1_n1_1_1	m_S_S_S_4_0_0_1	-0.3540
m_S_S_S_5_0_0_1	p_S_S_S_5_0_0_1	-0.3528
p_P_S_P_6_n1_1_1	p_S_S_S_4_0_0_1	+0.3510
m_P_S_P_6_n1_1_1	p_P_S_P_2_n1_1_1	-0.3503
p_S_S_S_2_0_0_1	p_S_S_S_5_0_0_1	+0.3489
p_P_S_P_4_n1_1_1	p_P_S_P_5_n1_1_1	-0.3418
m_S_S_S_1_0_0_1	m_S_S_S_5_0_0_1	+0.3406
m_S_S_S_0_0_0_1	p_S_S_S_3_0_0_1	+0.3355
m_S_S_S_4_0_0_1	p_S_S_S_3_0_0_1	-0.3349
m_P_S_P_3_n1_1_1	p_P_S_P_6_n1_1_1	+0.3339
p_S_S_S_5_0_0_1	p_S_S_S_6_0_0_1	+0.3304
m_P_S_P_2_n1_1_1	p_S_S_S_5_0_0_1	-0.3247
p_P_S_P_4_n1_1_1	p_S_S_S_1_0_0_1	-0.3231
m_P_S_P_5_n1_1_1	m_S_S_S_1_0_0_1	-0.3218
m_S_S_S_5_0_0_1	p_S_S_S_0_0_0_1	+0.3212
m_P_S_P_4_n1_1_1	m_S_S_S_6_0_0_1	+0.3175
m_S_S_S_2_0_0_1	m_S_S_S_3_0_0_1	-0.3163
m_P_S_P_4_n1_1_1	p_P_S_P_4_n1_1_1	-0.3122
m_P_S_P_4_n1_1_1	p_S_S_S_5_0_0_1	-0.3120
m_S_S_S_0_0_0_1	m_S_S_S_3_0_0_1	-0.3116
p_P_S_P_2_n1_1_1	p_S_S_S_5_0_0_1	+0.3093
m_S_S_S_4_0_0_1	p_P_S_P_6_n1_1_1	-0.3021
m_P_S_P_4_n1_1_1	m_S_S_S_2_0_0_1	-0.3012
p_P_S_P_2_n1_1_1	p_S_S_S_0_0_0_1	+0.3004
m_S_S_S_0_0_0_1	m_S_S_S_4_0_0_1	-0.2972
m_S_S_S_1_0_0_1	p_P_S_P_4_n1_1_1	-0.2969
m_P_S_P_1_n1_1_1	m_S_S_S_2_0_0_1	+0.2889
m_P_S_P_3_n1_1_1	p_S_S_S_0_0_0_1	-0.2884
m_S_S_S_1_0_0_1	p_S_S_S_2_0_0_1	-0.2883
m_P_S_P_6_n1_1_1	m_S_S_S_2_0_0_1	-0.2866
m_P_S_P_5_n1_1_1	m_S_S_S_5_0_0_1	-0.2827
m_P_S_P_1_n1_1_1	p_P_S_P_6_n1_1_1	-0.2816
m_P_S_P_3_n1_1_1	p_P_S_P_3_n1_1_1	-0.2779
m_P_S_P_4_n1_1_1	p_P_S_P_2_n1_1_1	-0.2727
m_S_S_S_2_0_0_1	p_S_S_S_2_0_0_1	+0.2725
m_S_S_S_1_0_0_1	p_S_S_S_0_0_0_1	+0.2705
m_P_S_P_3_n1_1_1	m_P_S_P_4_n1_1_1	+0.2705
p_P_S_P_2_n1_1_1	p_S_S_S_4_0_0_1	+0.2674
p_S_S_S_4_0_0_1	p_S_S_S_5_0_0_1	-0.2642
m_S_S_S_0_0_0_1	p_S_S_S_2_0_0_1	+0.2638
m_S_S_S_2_0_0_1	p_S_S_S_1_0_0_1	+0.2636
m_P_S_P_4_n1_1_1	p_P_S_P_6_n1_1_1	+0.2596
m_P_S_P_2_n1_1_1	p_S_S_S_4_0_0_1	+0.2592
m_P_S_P_1_n1_1_1	m_P_S_P_2_n1_1_1	-0.2487
m_S_S_S_6_0_0_1	p_P_S_P_5_n1_1_1	-0.2482
m_P_S_P_2_n1_1_1	m_P_S_P_4_n1_1_1	-0.2404
m_P_S_P_5_n1_1_1	m_S_S_S_0_0_0_1	-0.2387
m_P_S_P_4_n1_1_1	m_S_S_S_1_0_0_1	+0.2384
m_S_S_S_2_0_0_1	p_S_S_S_5_0_0_1	-0.2352
m_S_S_S_1_0_0_1	p_S_S_S_3_0_0_1	+0.2336
m_S_S_S_4_0_0_1	p_S_S_S_1_0_0_1	+0.2321
p_S_S_S_0_0_0_1	p_S_S_S_6_0_0_1	-0.2296
m_S_S_S_1_0_0_1	p_P_S_P_2_n1_1_1	-0.2236
m_S_S_S_3_0_0_1	p_P_S_P_4_n1_1_1	-0.2214
m_P_S_P_1_n1_1_1	p_P_S_P_3_n1_1_1	+0.2211
m_P_S_P_5_n1_1_1	m_S_S_S_6_0_0_1	+0.2203
m_S_S_S_4_0_0_1	p_P_S_P_5_n1_1_1	+0.2186
m_S_S_S_6_0_0_1	p_S_S_S_2_0_0_1	-0.2172
m_S_S_S_0_0_0_1	p_P_S_P_2_n1_1_1	+0.2147
m_P_S_P_5_n1_1_1	p_S_S_S_6_0_0_1	+0.2081
m_P_S_P_3_n1_1_1	m_S_S_S_3_0_0_1	-0.2055
m_P_S_P_2_n1_1_1	m_S_S_S_0_0_0_1	+0.2050
p_P_S_P_4_n1_1_1	p_S_S_S_3_0_0_1	+0.2047
m_S_S_S_3_0_0_1	p_S_S_S_6_0_0_1	+0.2041
m_P_S_P_3_n1_1_1	m_S_S_S_6_0_0_1	+0.2040
m_S_S_S_1_0_0_1	p_S_S_S_1_0_0_1	-0.2039
p_S_S_S_1_0_0_1	p_S_S_S_2_0_0_1	-0.2030
m_S_S_S_0_0_0_1	p_P_S_P_4_n1_1_1	+0.1992
m_P_S_P_6_n1_1_1	p_S_S_S_5_0_0_1	-0.1992
p_S_S_S_0_0_0_1	p_S_S_S_2_0_0_1	-0.1984
m_S_S_S_5_0_0_1	p_S_S_S_6_0_0_1	-0.1971
m_S_S_S_2_0_0_1	p_S_S_S_0_0_0_1	-0.1956
p_P_S_P_2_n1_1_1	p_P_S_P_5_n1_1_1	-0.1948
m_S_S_S_3_0_0_1	p_P_S_P_6_n1_1_1	-0.1930
p_S_S_S_3_0_0_1	p_S_S_S_6_0_0_1	-0.1918
p_P_S_P_3_n1_1_1	p_P_S_P_5_n1_1_1	+0.1914
m_S_S_S_1_0_0_1	m_S_S_S_3_0_0_1	-0.1890
m_S_S_S_4_0_0_1	p_S_S_S_4_0_0_1	-0.1884
p_S_S_S_4_0_0_1	p_S_S_S_6_0_0_1	-0.1824
m_P_S_P_6_n1_1_1	p_P_S_P_3_n1_1_1	-0.1815
m_P_S_P_1_n1_1_1	p_S_S_S_0_0_0_1	-0.1814
m_S_S_S_4_0_0_1	p_P_S_P_2_n1_1_1	-0.1793
p_P_S_P_2_n1_1_1	p_P_S_P_3_n1_1_1	-0.1783
m_P_S_P_3_n1_1_1	m_P_S_P_5_n1_1_1	-0.1759
m_S_S_S_6_0_0_1	p_P_S_P_3_n1_1_1	-0.1722
m_P_S_P_4_n1_1_1	p_S_S_S_1_0_0_1	+0.1715
m_P_S_P_5_n1_1_1	m_S_S_S_2_0_0_1	-0.1711
m_P_S_P_5_n1_1_1	p_S_S_S_5_0_0_1	+0.1711
m_P_S_P_2_n1_1_1	m_P_S_P_6_n1_1_1	-0.1697
m_S_S_S_5_0_0_1	p_S_S_S_2_0_0_1	+0.1650
m_S_S_S_4_0_0_1	m_S_S_S_6_0_0_1	-0.1649
m_P_S_P_1_n1_1_1	p_P_S_P_5_n1_1_1	+0.1644
m_S_S_S_3_0_0_1	m_S_S_S_5_0_0_1	-0.1641
m_P_S_P_3_n1_1_1	p_P_S_P_5_n1_1_1	-0.1638
m_P_S_P_2_n1_1_1	p_P_S_P_5_n1_1_1	-0.1618
m_S_S_S_2_0_0_1	p_P_S_P_6_n1_1_1	-0.1615
m_S_S_S_0_0_0_1	p_S_S_S_4_0_0_1	-0.1613
m_P_S_P_2_n1_1_1	p_P_S_P_3_n1_1_1	-0.1567
p_S_S_S_2_0_0_1	p_S_S_S_3_0_0_1	+0.1545
m_P_S_P_2_n1_1_1	p_S_S_S_1_0_0_1	-0.1543
m_P_S_P_4_n1_1_1	m_S_S_S_0_0_0_1	-0.1521
m_P_S_P_2_n1_1_1	p_S_S_S_6_0_0_1	-0.1512
m_S_S_S_4_0_0_1	m_S_S_S_5_0_0_1	+0.1510
m_P_S_P_6_n1_1_1	m_S_S_S_0_0_0_1	-0.1466
m_P_S_P_3_n1_1_1	m_S_S_S_2_0_0_1	-0.1466
m_S_S_S_1_0_0_1	p_P_S_P_5_n1_1_1	-0.1401
m_P_S_P_6_n1_1_1	m_S_S_S_1_0_0_1	+0.1388
p_P_S_P_5_n1_1_1	p_S_S_S_3_0_0_1	-0.1386
p_P_S_P_2_n1_1_1	p_S_S_S_1_0_0_1	-0.1351
p_P_S_P_3_n1_1_1	p_S_S_S_6_0_0_1	-0.1323
p_P_S_P_2_n1_1_1	p_P_S_P_6_n1_1_1	+0.1296
m_S_S_S_3_0_0_1	m_S_S_S_6_0_0_1	+0.1288
m_P_S_P_4_n1_1_1	p_S_S_S_3_0_0_1	-0.1233
m_P_S_P_6_n1_1_1	p_P_S_P_5_n1_1_1	-0.1218
p_P_S_P_2_n1_1_1	p_S_S_S_6_0_0_1	-0.1205
p_S_S_S_0_0_0_1	p_S_S_S_4_0_0_1	+0.1199
m_S_S_S_3_0_0_1	p_S_S_S_5_0_0_1	+0.1189
m_P_S_P_1_n1_1_1	p_P_S_P_4_n1_1_1	+0.1174
m_S_S_S_2_0_0_1	p_S_S_S_6_0_0_1	+0.1173
m_S_S_S_0_0_0_1	p_P_S_P_6_n1_1_1	+0.1152
m_S_S_S_3_0_0_1	p_P_S_P_2_n1_1_1	-0.1117
m_P_S_P_2_n1_1_1	m_S_S_S_5_0_0_1	+0.1102
p_P_S_P_6_n1_1_1	p_S_S_S_0_0_0_1	+0.1101
m_P_S_P_2_n1_1_1	p_S_S_S_3_0_0_1	+0.1079
p_P_S_P_2_n1_1_1	p_S_S_S_3_0_0_1	+0.1051
m_P_S_P_6_n1_1_1	p_S_S_S_1_0_0_1	+0.1025
m_P_S_P_5_n1_1_1	p_P_S_P_4_n1_1_1	+0.1021
p_P_S_P_4_n1_1_1	p_S_S_S_6_0_0_1	+0.1019
m_P_S_P_5_n1_1_1	p_S_S_S_1_0_0_1	+0.1019
m_P_S_P_3_n1_1_1	m_S_S_S_4_0_0_1	-0.1017
m_S_S_S_5_0_0_1	p_P_S_P_6_n1_1_1	-0.1002

		Param	1,1	1,-1	2,1	2,-1	3,1	3,-1	4,1	4,-1	5,1	5,-1	6,1	6,-1	0,0	1,0	2,0	3,0	4,0	5,0	6,0
Type	Source	dType
m	mean	num	1.055450	1.055450	0.754709	0.754709	0.444572	0.444572	0.697056	0.697056	0.384742	0.384742	0.365072	0.365072	1.650705	1.434879	1.513031	1.356301	1.345129	0.483148	0.150956
	ref	num	0.552053	0.552053	1.179614	1.179614	0.773419	0.773419	0.743628	0.743628	0.199496	0.199496	0.022219	0.022219	1.829333	0.485076	2.489088	0.577691	1.000426	0.236576	0.050528
	diff	%	47.695018	47.695018	56.300416	56.300416	73.969488	73.969488	6.681234	6.681234	48.148163	48.148163	93.913830	93.913830	10.821361	66.193949	64.510079	57.406851	25.626034	51.034553	66.527989
	diff	num	0.503397	0.503397	-0.424905	-0.424905	-0.328847	-0.328847	-0.046572	-0.046572	0.185246	0.185246	0.342853	0.342853	-0.178629	0.949803	-0.976057	0.778610	0.344703	0.246572	0.100428
	std	%	29.946654	29.946654	45.293117	45.293117	62.321106	62.321106	12.117068	12.117068	34.344714	34.344714	2.168152	2.168152	21.148320	14.386940	22.936991	10.259865	14.721248	39.056815	11.613643
	std	num	0.316072	0.316072	0.341831	0.341831	0.277062	0.277062	0.084463	0.084463	0.132138	0.132138	0.007915	0.007915	0.349096	0.206435	0.347044	0.139155	0.198020	0.188702	0.017532
	diff/std	%	159.266602	159.266602	124.302364	124.302364	118.690911	118.690911	55.139027	55.139027	140.190898	140.190898	4331.515603	4331.515603	51.168893	460.097493	281.249091	559.528328	174.075143	130.667472	572.843408
n	mean	num	0.255075	0.255075	0.182394	0.182394	0.107442	0.107442	0.168461	0.168461	0.092982	0.092982	0.088229	0.088229	0.398933	0.346774	0.365661	0.327783	0.325083	0.116765	0.036482
	ref	num	0.134106	0.134106	0.286555	0.286555	0.187881	0.187881	0.180644	0.180644	0.048462	0.048462	0.005397	0.005397	0.444387	0.117836	0.604657	0.140335	0.243026	0.057470	0.012274
	diff	%	47.424829	47.424829	57.107972	57.107972	74.868228	74.868228	7.232368	7.232368	47.880303	47.880303	93.882388	93.882388	11.393844	66.019301	65.359959	57.186805	25.241811	50.781629	66.355067
	diff	num	0.120969	0.120969	-0.104161	-0.104161	-0.080440	-0.080440	-0.012184	-0.012184	0.044520	0.044520	0.082831	0.082831	-0.045454	0.228938	-0.238996	0.187449	0.082057	0.059295	0.024208
	std	%	29.946769	29.946769	45.292977	45.292977	62.321130	62.321130	12.116934	12.116934	34.344739	34.344739	2.167948	2.167948	21.148450	14.386846	22.936949	10.259669	14.721217	39.057007	11.613524
	std	num	0.076387	0.076387	0.082612	0.082612	0.066959	0.066959	0.020412	0.020412	0.031935	0.031935	0.001913	0.001913	0.084368	0.049890	0.083871	0.033629	0.047856	0.045605	0.004237
	diff/std	%	158.363758	158.363758	126.085712	126.085712	120.132976	120.132976	59.688102	59.688102	139.410879	139.410879	4330.472681	4330.472681	53.875552	458.886539	284.954904	557.394275	171.465524	130.019254	571.360323
p	mean	num	-2.857393	-2.857393	-0.462299	-0.462299	0.413259	0.413259	1.358097	1.358097	0.292101	0.292101	1.187404	1.187404	0.282125	0.485615	0.400679	0.349399	0.217846	2.145848	-0.913459
	ref	num	-2.857393	-2.857393	1.843649	1.843649	-0.504852	-0.504852	-1.394619	-1.394619	1.889342	1.889342	2.309593	2.309593	-0.381123	1.609179	-2.177673	1.497285	-0.235498	2.108398	-2.401628
	diff	%	0.000000	0.000000	498.800404	498.800404	222.163669	222.163669	202.689219	202.689219	546.811368	546.811368	94.507767	94.507767	235.090249	231.369023	643.495002	328.532087	208.103055	1.745235	162.915649
	diff	num	0.000000	0.000000	-2.305947	-2.305947	0.918111	0.918111	2.752715	2.752715	-1.597241	-1.597241	-1.122189	-1.122189	0.663247	-1.123564	2.578352	-1.147887	0.453344	0.037450	1.488168
	std	%	0.000000	0.000000	273.973356	273.973356	332.628060	332.628060	164.360223	164.360223	340.159806	340.159806	127.655961	127.655961	638.659725	124.177131	325.572264	231.843985	710.531097	52.738871	265.561445
	std	num	0.000000	0.000000	1.266575	1.266575	1.374615	1.374615	2.232171	2.232171	0.993610	0.993610	1.515792	1.515792	1.801816	0.603023	1.304501	0.810060	1.547864	1.131696	2.425796
	diff/std	%	NaN	NaN	182.061647	182.061647	66.790417	66.790417	123.320117	123.320117	160.751317	160.751317	74.033179	74.033179	36.809938	186.321765	197.650436	141.703951	29.288381	3.309201	61.347629
pc	mean	num	0.000000	0.000000	1.984662	1.984662	2.086121	2.086121	1.156253	1.156253	2.306712	2.306712	2.067538	2.067538	1.535549	2.624980	2.018566	2.563737	1.755482	1.175693	1.086203
	ref	num	0.000000	0.000000	1.582143	1.582143	2.352541	2.352541	1.462774	1.462774	1.536450	1.536450	1.116199	1.116199	2.476270	1.816613	0.679721	1.928507	2.621895	1.317394	0.455766
	diff	%	NaN	NaN	20.281484	20.281484	12.771050	12.771050	26.509844	26.509844	33.392197	33.392197	46.013150	46.013150	61.262895	30.795166	66.326561	24.777526	49.354677	12.052520	58.040460
	diff	num	0.000000	0.000000	0.402519	0.402519	-0.266420	-0.266420	-0.306521	-0.306521	0.770262	0.770262	0.951339	0.951339	-0.940722	0.808367	1.338845	0.635231	-0.866413	-0.141701	0.630437
	std	%	NaN	NaN	41.142539	41.142539	36.277370	36.277370	110.641730	110.641730	16.840646	16.840646	66.532772	66.532772	25.330580	11.558246	22.407086	19.911869	17.613385	76.167236	124.266573
	std	num	0.000000	0.000000	0.816540	0.816540	0.756790	0.756790	1.279299	1.279299	0.388465	0.388465	1.375590	1.375590	0.388963	0.303402	0.452302	0.510488	0.309200	0.895493	1.349787
	diff/std	%	NaN	NaN	49.295655	49.295655	35.203903	35.203903	23.960077	23.960077	198.283347	198.283347	69.158625	69.158625	241.853501	266.434593	296.007080	124.435966	280.211202	15.823759	46.706414

Param	P_S_P_1_1_n1_1	P_S_P_1_n1_1_1	P_S_P_2_1_n1_1	P_S_P_2_n1_1_1	P_S_P_3_1_n1_1	P_S_P_3_n1_1_1	P_S_P_4_1_n1_1	P_S_P_4_n1_1_1	P_S_P_5_1_n1_1	P_S_P_5_n1_1_1	P_S_P_6_1_n1_1	P_S_P_6_n1_1_1	S_S_S_0_0_0_1	S_S_S_1_0_0_1	S_S_S_2_0_0_1	S_S_S_3_0_0_1	S_S_S_4_0_0_1	S_S_S_5_0_0_1	S_S_S_6_0_0_1
count	24.000000	24.000000	24.000000	24.000000	24.000000	24.000000	24.000000	24.000000	24.000000	24.000000	24.000000	24.000000	24.000000	24.000000	24.000000	24.000000	24.000000	24.000000	24.000000
mean	-0.386717	-0.386717	0.614867	0.614867	0.762848	0.762848	0.844967	0.844967	0.769134	0.769134	0.927061	0.927061	0.966828	1.223062	1.074484	1.149305	0.910885	0.980364	0.090045
std	1.518035	1.518035	1.169028	1.169028	1.086327	1.086327	1.288277	1.288277	1.041803	1.041803	1.238466	1.238466	1.086054	0.987656	0.987073	1.039141	1.000374	1.041297	1.482789
min	-2.857393	-2.857393	-2.088247	-2.088247	-1.181846	-1.181846	-2.354861	-2.354861	-0.552797	-0.552797	0.040184	0.040184	-1.661460	-0.661391	-1.072057	-1.253769	-1.368626	-0.028552	-3.141593
25%	-0.714348	-0.714348	0.152516	0.152516	0.145319	0.145319	0.184532	0.184532	0.106647	0.106647	0.090995	0.090995	0.412534	0.399965	0.377625	0.358271	0.335998	0.167667	0.032309
50%	0.064960	0.064960	0.418696	0.418696	0.458164	0.458164	0.536412	0.536412	0.393872	0.393872	0.358630	0.358630	1.328339	1.102212	1.246898	1.007032	1.315784	0.545255	0.132213
75%	0.408844	0.408844	1.142176	1.142176	1.509632	1.509632	0.846888	0.846888	1.653091	1.653091	1.113173	1.113173	1.739908	1.810177	1.851960	1.534766	1.545208	1.535203	0.220275
max	1.514140	1.514140	3.047565	3.047565	3.078588	3.078588	3.141593	3.141593	2.994350	2.994350	3.141593	3.141593	2.461075	2.992687	2.444431	3.132916	2.113221	3.141593	3.141593

			Type	m	n	p	pc	comp	compC	labels
Cont	l	m	mu
P	1	-1	1	1.055450	0.255075	-2.857393	0.000000	-1.013112-0.295937j	0.255075+0.000000j	1,-1
	1	1	-1	1.055450	0.255075	-2.857393	0.000000	-1.013112-0.295937j	0.255075+0.000000j	1,1
	2	-1	1	0.754709	0.182394	-0.462299	1.984662	0.675487-0.336605j	-0.073350+0.166995j	2,-1
	2	1	-1	0.754709	0.182394	-0.462299	1.984662	0.675487-0.336605j	-0.073350+0.166995j	2,1
	3	-1	1	0.444572	0.107442	0.413259	2.086121	0.407146+0.178538j	-0.052949+0.093488j	3,-1
	3	1	-1	0.444572	0.107442	0.413259	2.086121	0.407146+0.178538j	-0.052949+0.093488j	3,1
	4	-1	1	0.697056	0.168461	1.358097	1.156253	0.147148+0.681347j	0.067851+0.154192j	4,-1
	4	1	-1	0.697056	0.168461	1.358097	1.156253	0.147148+0.681347j	0.067851+0.154192j	4,1
	5	-1	1	0.384742	0.092982	0.292101	2.306712	0.368444+0.110792j	-0.062416+0.068920j	5,-1
	5	1	-1	0.384742	0.092982	0.292101	2.306712	0.368444+0.110792j	-0.062416+0.068920j	5,1
	6	-1	1	0.365072	0.088229	1.187404	2.067538	0.136562+0.338568j	-0.042047+0.077565j	6,-1
	6	1	-1	0.365072	0.088229	1.187404	2.067538	0.136562+0.338568j	-0.042047+0.077565j	6,1
S	0	0	0	1.650705	0.398933	0.282125	1.535549	1.585446+0.459551j	0.014058+0.398686j	0,0
	1	0	0	1.434879	0.346774	0.485615	2.624980	1.268989+0.669734j	-0.301519+0.171285j	1,0
	2	0	0	1.513031	0.365661	0.400679	2.018566	1.393193+0.590149j	-0.158315+0.329612j	2,0
	3	0	0	1.356301	0.327783	0.349399	2.563737	1.274352+0.464306j	-0.274563+0.179045j	3,0
	4	0	0	1.345129	0.325083	0.217846	1.755482	1.313337+0.290719j	-0.059698+0.319555j	4,0
	5	0	0	0.483148	0.116765	2.145848	1.175693	-0.262774+0.405440j	0.044943+0.107769j	5,0
	6	0	0	0.150956	0.036482	-0.913459	1.086203	0.092236-0.119500j	0.016995+0.032282j	6,0

Thu Apr 11 17:15:26 2024 EDT
OS	Linux	CPU(s)	56	Machine	x86_64	Architecture	64bit
RAM	78.6 GiB	Environment	Jupyter
Python 3.10.11 \| packaged by conda-forge \| (main, May 10 2023, 18:58:44) [GCC 11.3.0]
epsproc	1.3.2.dev0	xarray	2022.3.0	jupyter	Version unknown	holoviews	1.18.3
pandas	1.5.3	numpy	1.23.5	scipy	1.10.1	IPython	8.13.2
matplotlib	3.5.3	scooby	0.9.2